The Data Governance Imperative

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OCDQ Radio is a vendor-neutral podcast about data quality and its related disciplines, produced and hosted by Jim Harris.

During this episode, Steve Sarsfield and I discuss how data governance is about changing the hearts and minds of your company to see the value of data quality, the characteristics of a data champion, and creating effective data quality scorecards.

Steve Sarsfield is a leading author and expert in data quality and data governance.  His book The Data Governance Imperative is a comprehensive exploration of data governance focusing on the business perspectives that are important to data champions, front-office employees, and executives.  He runs the Data Governance and Data Quality Insider, which is an award-winning and world-recognized blog.  Steve Sarsfield is the Product Marketing Manager for Data Governance and Data Quality at Talend.

Popular OCDQ Radio Episodes

Clicking on the link will take you to the episode’s blog post:

  • Demystifying Data Science — Guest Melinda Thielbar, a Ph.D. Statistician, discusses what a data scientist does and provides a straightforward explanation of key concepts such as signal-to-noise ratio, uncertainty, and correlation.
  • Data Quality and Big Data — Guest Tom Redman (aka the “Data Doc”) discusses Data Quality and Big Data, including if data quality matters less in larger data sets, and if statistical outliers represent business insights or data quality issues.
  • Demystifying Master Data Management — Guest John Owens explains the three types of data (Transaction, Domain, Master), the four master data entities (Party, Product, Location, Asset), and the Party-Role Relationship, which is where we find many of the terms commonly used to describe the Party master data entity (e.g., Customer, Supplier, Employee).
  • Data Governance Star Wars — Special Guests Rob Karel and Gwen Thomas joined this extended, and Star Wars themed, discussion about how to balance bureaucracy and business agility during the execution of data governance programs.
  • The Johari Window of Data Quality — Guest Martin Doyle discusses helping people better understand their data and assess its business impacts, not just the negative impacts of bad data quality, but also the positive impacts of good data quality.
  • Studying Data Quality — Guest Gordon Hamilton discusses the key concepts from recommended data quality books, including those which he has implemented in his career as a data quality practitioner.

The UX Factor

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This blog post is sponsored by the Enterprise CIO Forum and HP.

In his book The Most Human Human, Brian Christian explained that “UX — short for User Experience — refers to the experience a given user has using a piece of software or technology, rather than the purely technical capacities of that device.”

But since its inception, the computer industry has been primarily concerned with technical capacities.  Computer advancements have followed the oft-cited Moore’s Law, a trend accurately described by Intel co-founder Gordon Moore in 1965, which states the number of transistors that can be placed inexpensively on an integrated circuit, thereby increasing processing speed and memory capacity, doubles approximately every two years.

However, as Christian explained, for a while in the computer industry, “an arms race between hardware and software created the odd situation that computers were getting exponentially faster but not faster at all to use, as software made ever-larger demands on systems resources, at a rate that matched and sometimes outpaced hardware improvements.”  This was sometimes called “Andy and Bill’s Law,” referring to Andy Grove of Intel and Bill Gates of Microsoft.  “What Andy giveth, Bill taketh away.”

But these advancements in computational power, along with increased network bandwidth, parallel processing frameworks (e.g., Hadoop), scalable and distributed models (e.g., cloud computing), and other advancements (e.g., in-memory technology) are making powerful technical capacities so much more commonplace, and so much less expensive, that the computer industry is responding to consumers demanding that the primary concern be user experience — hence the so-called Consumerization of IT.

“As computing technology moves increasingly toward mobile devices,” Christian noted, “product development becomes less about the raw computing horsepower and more about the overall design of the product and its fluidity, reactivity, and ease of use.”

David Snow and Alex Bakker have recently blogged about the challenges and opportunities facing enterprises and vendors with respect to the Bring Your Own Device (BYOD) movement, where more employees, and employers, are embracing mobile devices.

Although the old mantra of function over form is not getting replaced by form over function, form factor, interface design, and the many other aspects of User Experience are becoming the unrelenting UX Factor of the continuing consumerization trend.

This blog post is sponsored by the Enterprise CIO Forum and HP.

 

Related Posts

The Diderot Effect of New Technology

A Swift Kick in the AAS

Shadow IT and the New Prometheus

The IT Consumerization Conundrum

The IT Prime Directive of Business First Contact

Are Cloud Providers the Bounty Hunters of IT?

Are Applications the La Brea Tar Pits for Data?

Why does the sun never set on legacy applications?

The IT Pendulum and the Federated Future of IT

Suburban Flight, Technology Sprawl, and Garage IT

Data Driven

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OCDQ Radio is a vendor-neutral podcast about data quality and its related disciplines, produced and hosted by Jim Harris.

This is Part 1 of 2 from my recent discussion with Tom Redman.  In this episode, Tom and I discuss concepts from one of my favorite data quality books, which is his most recent book: Data Driven: Profiting from Your Most Important Business Asset.

Our discussion includes viewing data as an asset, an organization’s hierarchy of data needs, a simple model for culture change, and attempting to achieve the “single version of the truth” being marketed as a goal of master data management (MDM).

Dr. Thomas C. Redman (the “Data Doc”) is an innovator, advisor, and teacher.  He was first to extend quality principles to data and information in the late 80s.  Since then he has crystallized a body of tools, techniques, roadmaps and organizational insights that help organizations make order-of-magnitude improvements.

More recently Tom has developed keen insights into the nature of data and formulated the first comprehensive approach to “putting data to work.”  Taken together, these enable organizations to treat data as assets of virtually unlimited potential.

Tom has personally helped dozens of leaders and organizations better understand data and data quality and start their data programs.  He is a sought-after lecturer and the author of dozens of papers and four books.

Prior to forming Navesink Consulting Group in 1996, Tom conceived the Data Quality Lab at AT&T Bell Laboratories in 1987 and led it until 1995. Tom holds a Ph.D. in statistics from Florida State University.  He holds two patents.

Popular OCDQ Radio Episodes

Clicking on the link will take you to the episode’s blog post:

  • Demystifying Data Science — Guest Melinda Thielbar, a Ph.D. Statistician, discusses what a data scientist does and provides a straightforward explanation of key concepts such as signal-to-noise ratio, uncertainty, and correlation.
  • Data Quality and Big Data — Guest Tom Redman (aka the “Data Doc”) discusses Data Quality and Big Data, including if data quality matters less in larger data sets, and if statistical outliers represent business insights or data quality issues.
  • Demystifying Master Data Management — Guest John Owens explains the three types of data (Transaction, Domain, Master), the four master data entities (Party, Product, Location, Asset), and the Party-Role Relationship, which is where we find many of the terms commonly used to describe the Party master data entity (e.g., Customer, Supplier, Employee).
  • Data Governance Star Wars — Special Guests Rob Karel and Gwen Thomas joined this extended, and Star Wars themed, discussion about how to balance bureaucracy and business agility during the execution of data governance programs.
  • The Johari Window of Data Quality — Guest Martin Doyle discusses helping people better understand their data and assess its business impacts, not just the negative impacts of bad data quality, but also the positive impacts of good data quality.
  • Studying Data Quality — Guest Gordon Hamilton discusses the key concepts from recommended data quality books, including those which he has implemented in his career as a data quality practitioner.

Data Quality and Miracle Exceptions

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“Reading superhero comic books with the benefit of a Ph.D. in physics,” James Kakalios explained in The Physics of Superheroes, “I have found many examples of the correct description and application of physics concepts.  Of course, the use of superpowers themselves involves direct violations of the known laws of physics, requiring a deliberate and willful suspension of disbelief.”

“However, many comics need only a single miracle exception — one extraordinary thing you have to buy into — and the rest that follows as the hero and the villain square off would be consistent with the principles of science.”

“Data Quality is all about . . .”

It is essential to foster a marketplace of ideas about data quality in which a diversity of viewpoints is freely shared without bias, where everyone is invited to get involved in discussions and debates and have an opportunity to hear what others have to offer.

However, one of my biggest pet peeves about the data quality industry is when I listen to analysts, vendors, consultants, and other practitioners discuss data quality challenges, I am often required to make a miracle exception for data quality.  In other words, I am given one extraordinary thing I have to buy into in order to be willing to buy their solution to all of my data quality problems.

These superhero comic book style stories usually open with a miracle exception telling me that “data quality is all about . . .”

Sometimes, the miracle exception is purchasing technology from the right magic quadrant.  Other times, the miracle exception is either following a comprehensive framework, or following the right methodology from the right expert within the right discipline (e.g., data modeling, business process management, information quality management, agile development, data governance, etc.).

But I am especially irritated by individuals who bash vendors for selling allegedly only reactive data cleansing tools, while selling their allegedly only proactive defect prevention methodology, as if we could avoid cleaning up the existing data quality issues, or we could shut down and restart our organizations, so that before another single datum is created or business activity is executed, everyone could learn how to “do things the right way” so that “the data will always be entered right, the first time, every time.”

Although these and other miracle exceptions do correctly describe the application of data quality concepts in isolation, by doing so, they also oversimplify the multifaceted complexity of data quality, requiring a deliberate and willful suspension of disbelief.

Miracle exceptions certainly make for more entertaining stories and more effective sales pitches, but oversimplifying complexity for the purposes of explaining your approach, or, even worse and sadly more common, preaching at people that your approach definitively solves their data quality problems, is nothing less than applying the principle of deus ex machina to data quality.

Data Quality and deus ex machina

Deus ex machina is a plot device whereby a seemingly unsolvable problem is suddenly and abruptly solved with the contrived and unexpected intervention of some new event, character, ability, or object.

This technique is often used in the marketing of data quality software and services, where the problem of poor data quality can seemingly be solved by a new event (e.g., creating a data governance council), a new character (e.g., hiring an expert consultant), a new ability (e.g., aligning data quality metrics with business insight), or a new object (e.g., purchasing a new data quality tool).

Now, don’t get me wrong.  I do believe various technologies and methodologies from numerous disciplines, as well as several core principles (e.g., communication, collaboration, and change management) are all important variables in the data quality equation, but I don’t believe that any particular variable can be taken in isolation and deified as the God Particle of data quality physics.

Data Quality is Not about One Extraordinary Thing

Data quality isn’t all about technology, nor is it all about methodology.  And data quality isn’t all about data cleansing, nor is it all about defect prevention.  Data quality is not about only one thing — no matter how extraordinary any one of its things may seem.

Battling the dark forces of poor data quality doesn’t require any superpowers, but it does require doing the hard daily work of continuously improving your data quality.  Data quality does not have a miracle exception, so please stop believing in one.

And for the love of high-quality data everywhere, please stop trying to sell us one.

Magic Elephants, Data Psychics, and Invisible Gorillas

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This blog post is sponsored by the Enterprise CIO Forum and HP.

A recent Forbes article predicts Big Data will be a $50 billion market by 2017, and Michael Friedenberg recently blogged how the rise of big data is generating buzz about Hadoop (which I call the Magic Elephant): “It certainly looks like the Holy Grail for organizing unstructured data, so it’s no wonder everyone is jumping on this bandwagon.  So get ready for Hadoopalooza 2012.”

John Burke recently blogged about the role of big data helping CIOs “figure out how to handle the new, the unusual, and the unexpected as an opportunity to focus more clearly on how to bring new levels of order to their traditional structured data.”

As I have previously blogged, many big data proponents (especially the Big Data Lebowski vendors selling Hadoop solutions) extol its virtues as if big data provides clairvoyant business insight, as if big data was the Data Psychic of the Information Age.

But a recent New York Times article opened with the story of a statistician working for a large retail chain being asked by his marketing colleagues: “If we wanted to figure out if a customer is pregnant, even if she didn’t want us to know, can you do that?” As Eric Siegel of Predictive Analytics World is quoted in the article, “we’re living through a golden age of behavioral research.  It’s amazing how much we can figure out about how people think now.”

So, perhaps calling big data psychic is not so far-fetched after all.  However, the potential of predictive analytics exemplifies why one of the biggest implications about big data is the data privacy concerns it raises.

Although it’s amazing (and scary) how much the Data Psychic can figure out about how we think (and work, shop, vote, love), it’s equally amazing (and scary) how much Psychology is figuring out about how we think, how we behave, and how we decide.

As I recently blogged about WYSIATI (“what you see is all there is” from Daniel Kahneman’s book Thinking, Fast and Slow), when you are using big data to make business decisions, what you are looking for can greatly influence what you are looking at (and vice versa).  But this natural human tendency could cause you miss the Invisible Gorilla walking across your screen.

If you are unfamiliar with that psychology experiment, which was created by Christopher Chabris and Daniel Simons, authors of the book The Invisible Gorilla: How Our Intuitions Deceive Us, then I recommend going to theinvisiblegorilla.com/videos.html. (By the way, before I was familiar with its premise, the first time I watched the video, I did not see the guy in the gorilla suit, and now when I watch the video, seeing the “invisible gorilla” distracts me, causing me to not count the number of passes correctly.)

In his book Incognito: The Secret Lives of the Brain, David Eagleman explained how our brain samples just a small bit of the physical world, making time-saving assumptions and seeing only as well as it needs to.  As our eyes interrogate the world, they optimize their strategy for the incoming data, arbitrating a battle between the conflicting information.  What we see is not what is really out there, but instead only a moment-by-moment version of which perception is winning over the others.  Our perception works not by building up bits of captured data, but instead by matching our expectations to the incoming sensory data.

I don’t doubt the Magic Elephants and Data Psychics provide the potential to envision and analyze almost anything happening within the complex and constantly changing business world — as well as the professional and personal lives of the people in it.

But I am concerned that information optimization driven by the biases of our human intuition and perception will only match our expectations to those fast-moving large volumes of various data, thereby causing us to not see many of the Invisible Gorillas.

Although this has always been a business intelligence concern, as technological advancements improve our data analytical tools, we must not lose sight of the fact that tools and data remain only as effective (and as beneficent) as the humans who wield them.

This blog post is sponsored by the Enterprise CIO Forum and HP.

 

Related Posts

Big Data el Memorioso

Neither the I Nor the T is Magic

Information Overload Revisited

HoardaBytes and the Big Data Lebowski

WYSIWYG and WYSIATI

The Speed of Decision

The Data-Decision Symphony

A Decision Needle in a Data Haystack

The Big Data Collider

Dot Collectors and Dot Connectors

DQ-View: Data Is as Data Does

Data, Information, and Knowledge Management

The Algebra of Collaboration

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Most organizations have a vertical orientation, which creates a division of labor between functional areas where daily operations are carried out by people who have been trained in a specific type of business activity (e.g., Product Manufacturing, Marketing, Sales, Finance, Customer Service).  However, according to the most basic enterprise arithmetic, the sum of all vertical functions is one horizontal organization.  For example, in an organization with five vertical functions, 1 + 1 + 1 + 1 + 1 = 1 (and not 5).

Other times, it seems like division is the only mathematics the enterprise understands, creating perceived organizational divides based on geography (e.g., the Boston office versus the London office), or hierarchy (e.g., management versus front-line workers), or the Great Rift known as the Business versus IT.

However, enterprise-wide initiatives, such as data quality and data governance, require a cross-functional alignment reaching horizontally across the organization’s vertical functions, fostering a culture of collaboration combining a collective ownership with a shared responsibility and an individual accountability, requiring a branch of mathematics I call the Algebra of Collaboration.

For starters, as James Kakalios explained in his super book The Physics of Superheroes, “there is a trick to algebra: If one has an equation describing a true statement, such as 1 = 1, then one can add, subtract, multiply, or divide (excepting division by zero) the equation by any number we wish, and as long as we do it to both the left and right sides of the equation, the correctness of the equation is unchanged.  So if we add 2 to both sides of 1 = 1, we obtain 1 + 2 = 1 + 2 or 3 = 3, which is still a true statement.”

So, in the Algebra of Collaboration, we first establish one of the organization’s base equations, its true statements, for example, using the higher order collaborative equation that attempts to close the Great Rift otherwise known as the IT-Business Chasm:

Business = IT

Then we keep this base equation balanced by performing the same operation on both the left and right sides, for example:

Business + Data Quality + Data Governance = IT + Data Quality + Data Governance

The point is that everyone, regardless of their primary role or vertical function, must accept a shared responsibility for preventing data quality lapses and for responding appropriately to mitigate the associated business risks when issues occur.

Now, of course, as I blogged about in The Stakeholder’s Dilemma, this equation does not always remain perfectly balanced at all times.  The realities of the fiscal calendar effect, conflicting interests, and changing business priorities, will mean that the amount of resources (money, time, people) added to the equation by a particular stakeholder, vertical function, or group will vary.

But it’s important to remember the true statement that the base equation represents.  The trick of algebra is just one of the tricks of the collaboration trade.  Organizations that are successful with data quality and data governance view collaboration not just as a guiding principle, but also as a call to action in their daily practices.

Is your organization practicing the Algebra of Collaboration?

 

Related Posts

The Business versus IT—Tear down this wall!

The Road of Collaboration

The Collaborative Culture of Data Governance

Collaboration isn’t Brain Surgery

Finding Data Quality

Being Horizontally Vertical

The Year of the Datechnibus

Dot Collectors and Dot Connectors

No Datum is an Island of Serendip

The Three Most Important Letters in Data Governance

The Stakeholder’s Dilemma

Are you Building Bridges or Digging Moats?

Has Data Become a Four-Letter Word?

The Data Governance Oratorio

Video: Declaration of Data Governance

Decision Management Systems

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OCDQ Radio is a vendor-neutral podcast about data quality and its related disciplines, produced and hosted by Jim Harris.

During this episode, I discuss decision management with James Taylor, author of the new book Decision Management Systems: A Practical Guide to Using Business Rules and Predictive Analytics.

James Taylor is the CEO of Decision Management Solutions, and the leading expert in Decision Management Systems, which are active participants in improving business results by applying business rules, predictive analytics, and optimization technologies to address the toughest issues facing businesses today, and changing the way organizations are doing business.

James Taylor has led Decision Management efforts for leading companies in insurance, banking, health management, and telecommunications.  Decision Management Solutions works with clients to improve their business by applying analytics and business rules technology to automate and improve decisions.  Clients range from start-ups and software companies to major North American insurers, a travel company, the health management division of a major healthcare company, one of Europe’s largest banks, and several major decision management technology vendors.

Popular OCDQ Radio Episodes

Clicking on the link will take you to the episode’s blog post:

  • Demystifying Data Science — Guest Melinda Thielbar, a Ph.D. Statistician, discusses what a data scientist does and provides a straightforward explanation of key concepts such as signal-to-noise ratio, uncertainty, and correlation.
  • Data Quality and Big Data — Guest Tom Redman (aka the “Data Doc”) discusses Data Quality and Big Data, including if data quality matters less in larger data sets, and if statistical outliers represent business insights or data quality issues.
  • Demystifying Master Data Management — Guest John Owens explains the three types of data (Transaction, Domain, Master), the four master data entities (Party, Product, Location, Asset), and the Party-Role Relationship, which is where we find many of the terms commonly used to describe the Party master data entity (e.g., Customer, Supplier, Employee).
  • Data Governance Star Wars — Special Guests Rob Karel and Gwen Thomas joined this extended, and Star Wars themed, discussion about how to balance bureaucracy and business agility during the execution of data governance programs.
  • The Johari Window of Data Quality — Guest Martin Doyle discusses helping people better understand their data and assess its business impacts, not just the negative impacts of bad data quality, but also the positive impacts of good data quality.
  • Studying Data Quality — Guest Gordon Hamilton discusses the key concepts from recommended data quality books, including those which he has implemented in his career as a data quality practitioner.

Dot Collectors and Dot Connectors

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The attention blindness inherent in the digital age often leads to a debate about multitasking, which many claim impairs our ability to solve complex problems.  Therefore, we often hear that we need to adopt monotasking, i.e., we need to eliminate all possible distractions and focus our attention on only one task at a time.

However, during the recent Harvard Business Review podcast The Myth of Monotasking, Cathy Davidson, author of the new book Now You See It: How the Brain Science of Attention Will Transform the Way We Live, Work, and Learn, explained how “the moment that you start not paying attention fully to the task at hand, you actually start seeing other things that your attention would have missed.”  Although Davidson acknowledges that attention blindness is a serious problem, she explained that there really is no such thing as monotasking.  Modern neuroscience research has revealed that the human brain is, in fact, always multitasking.  Furthermore, she explained how multitasking can be extremely useful for a new and expansive form of attention.

“We all see selectively, but we don’t select the same things to see,” Davidson explained.  “So if we can learn to work together, we can actually account for, and productively work around, our own individual attention blindness by seeing collaboratively in a way that compensates for that blindness.”

During the podcast, an analogy was made that focusing attention on specific tasks can result in a lot of time spent collecting dots without spending enough time connecting those dots.  This point caused me to ponder the division of organizational labor that has historically existed between the dot collection of data management, which focuses on aspects such as data integrity and data quality, and the dot connection of business intelligence, which focuses on aspects such as data analysis and data visualization.

I think most data management professionals are dot collectors since it often seems like they spend a lot of their time, money, and attention on collecting (and profiling, modeling, cleansing, transforming, matching, and otherwise managing) data dots.

But since data’s value comes from data’s usefulness, merely collecting data dots doesn’t mean anything if you cannot connect those dots into meaningful patterns that enable your organization to take action or otherwise support your business activities.

So I think most business intelligence professionals are dot connectors since it often seems like they spend a lot of their time, money, and attention on connecting (and querying, aggregating, reporting, visualizing, and otherwise analyzing) data dots.

However, the attention blindness of data management and business intelligence professionals means that they see selectively, often intentionally selecting to not see the same things.  But as more of our personal and professional lives become digitized and pixelated, the big picture of the business world is inundated with the multifaceted challenges of big data, where the fast-moving large volumes of varying data are transforming the way we have to view traditional data management and business intelligence.

We need to replace our perspective of data management and business intelligence as separate monotasking activities with an expansive form of organizational multitasking where the dot collectors and dot connectors work together more collaboratively.

 

Related Posts

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DQ-View: Data Is as Data Does

The Real Data Value is Business Insight

Information Overload Revisited

Neither the I Nor the T is Magic

The Big Data Collider

OCDQ Radio - Big Data and Big Analytics

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The Interconnected User Interface

DQ-View: Data Is as Data Does

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Data Quality (DQ) View is an OCDQ regular segment.  Each DQ-View is a brief video discussion of a data quality key concept.

If you are having trouble viewing this video, then you can watch it on Vimeo by clicking on this link: DQ-View on Vimeo

 

The following list contains the books shown in the video, simply listed in the order they appeared on my book shelf:

 

Previous DQ-View Videos

You can also watch a regularly updated page of my videos by clicking on this link: OCDQ Videos

DQ-View: Baseball and Data Quality

DQ-View: Occam’s Razor Burn

DQ-View: Roman Ruts on the Road to Data Governance

DQ-View: Talking about Data

DQ-View: The Poor Data Quality Blizzard

DQ-View: New Data Resolutions

DQ-View: From Data to Decision

DQ View: Achieving Data Quality Happiness

Data Quality is not a Magic Trick

DQ-View: The Cassandra Effect

DQ-View: Is Data Quality the Sun?

DQ-View: Designated Asker of Stupid Questions

Video: Oh, the Data You’ll Show!

Making EIM Work for Business

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OCDQ Radio is a vendor-neutral podcast about data quality and its related disciplines, produced and hosted by Jim Harris.

During this episode, I discuss Enterprise Information Management (EIM) with John Ladley, the author of the excellent book Making EIM Work for Business, exploring what makes information management, not just useful, but valuable to the enterprise.

John Ladley is a business technology thought leader with 30 years of experience in improving organizations through the successful implementation of information systems.  He is a recognized authority in the use and implementation of business intelligence and enterprise information management.  John Ladley frequently writes and speaks on a variety of technology and enterprise information management topics.  His information management experience is balanced between strategic technology planning, project management, and, most important, the practical application of technology to business problems.

Popular OCDQ Radio Episodes

Clicking on the link will take you to the episode’s blog post:

  • Demystifying Data Science — Guest Melinda Thielbar, a Ph.D. Statistician, discusses what a data scientist does and provides a straightforward explanation of key concepts such as signal-to-noise ratio, uncertainty, and correlation.
  • Data Quality and Big Data — Guest Tom Redman (aka the “Data Doc”) discusses Data Quality and Big Data, including if data quality matters less in larger data sets, and if statistical outliers represent business insights or data quality issues.
  • Demystifying Master Data Management — Guest John Owens explains the three types of data (Transaction, Domain, Master), the four master data entities (Party, Product, Location, Asset), and the Party-Role Relationship, which is where we find many of the terms commonly used to describe the Party master data entity (e.g., Customer, Supplier, Employee).
  • Data Governance Star Wars — Special Guests Rob Karel and Gwen Thomas joined this extended, and Star Wars themed, discussion about how to balance bureaucracy and business agility during the execution of data governance programs.
  • The Johari Window of Data Quality — Guest Martin Doyle discusses helping people better understand their data and assess its business impacts, not just the negative impacts of bad data quality, but also the positive impacts of good data quality.
  • Studying Data Quality — Guest Gordon Hamilton discusses the key concepts from recommended data quality books, including those which he has implemented in his career as a data quality practitioner.

You only get a Return from something you actually Invest in

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In my previous post, I took a slightly controversial stance on a popular three-word phrase — Root Cause Analysis.  In this post, it’s another popular three-word phrase — Return on Investment (most commonly abbreviated as the acronym ROI).

What is the ROI of purchasing a data quality tool or launching a data governance program?

Zero.  Zip.  Zilch.  Intet.  Ingenting.  Rien.  Nada.  Nothing.  Nichts.  Niets.  Null.  Niente.  Bupkis.

There is No Such Thing as the ROI of purchasing a data quality tool or launching a data governance program.

Before you hire “The Butcher” to eliminate me for being The Man Who Knew Too Little about ROI, please allow me to explain.

Returns only come from Investments

Although the reason that you likely purchased a data quality tool is because you have business-critical data quality problems, simply purchasing a tool is not an investment (unless you believe in Magic Beans) since the tool itself is not a solution.

You use tools to build, test, implement, and maintain solutions.  For example, I spent several hundred dollars on new power tools last year for a home improvement project.  However, I haven’t received any return on my home improvement investment for a simple reason — I still haven’t even taken most of the tools out of their packaging yet.  In other words, I barely even started my home improvement project.  It is precisely because I haven’t invested any time and effort that I haven’t seen any returns.  And it certainly isn’t going to help me (although it would help Home Depot) if I believed buying even more new tools was the answer.

Although the reason that you likely launched a data governance program is because you have complex issues involving the intersection of data, business processes, technology, and people, simply launching a data governance program is not an investment since it does not conjure the three most important letters.

Data is only an Asset if Data is a Currency

In his book UnMarketing, Scott Stratten discusses this within the context of the ROI of social media (a commonly misunderstood aspect of social media strategy), but his insight is just as applicable to any discussion of ROI.  “Think of it this way: You wouldn’t open a business bank account and ask to withdraw $5,000 before depositing anything. The banker would think you are a loony.”

Yet, as Stratten explained, people do this all the time in social media by failing to build up what is known as social currency.  “You’ve got to invest in something before withdrawing. Investing your social currency means giving your time, your knowledge, and your efforts to that channel before trying to withdraw monetary currency.”

The same logic applies perfectly to data quality and data governance, where we could say it’s the failure to build up what I will call data currency.  You’ve got to invest in data before you could ever consider data an asset to your organization.  Investing your data currency means giving your time, your knowledge, and your efforts to data quality and data governance before trying to withdraw monetary currency (i.e., before trying to calculate the ROI of a data quality tool or a data governance program).

If you actually want to get a return on your investment, then actually invest in your data.  Invest in doing the hard daily work of continuously improving your data quality and putting into practice your data governance principles, policies, and procedures.

Data is only an asset if data is a currency.  Invest in your data currency, and you will eventually get a return on your investment.

You only get a return from something you actually invest in.

Related Posts

Can Enterprise-Class Solutions Ever Deliver ROI?

Do you believe in Magic (Quadrants)?

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What Data Quality Technology Wants

A Farscape Analogy for Data Quality

The Data Quality Wager

“Some is not a number and soon is not a time”

The Dumb and Dumber Guide to Data Quality

There is No Such Thing as a Root Cause

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Root cause analysis.  Most people within the industry, myself included, often discuss the importance of determining the root cause of data governance and data quality issues.  However, the complex cause and effect relationships underlying an issue means that when an issue is encountered, often you are only seeing one of the numerous effects of its root cause (or causes).

In my post The Root! The Root! The Root Cause is on Fire!, I poked fun at those resistant to root cause analysis with the lyrics:

The Root! The Root! The Root Cause is on Fire!
We don’t want to determine why, just let the Root Cause burn.
Burn, Root Cause, Burn!

However, I think that the time is long overdue for even me to admit the truth — There is No Such Thing as a Root Cause.

Before you charge at me with torches and pitchforks for having an Abby Normal brain, please allow me to explain.

 

Defect Prevention, Mouse Traps, and Spam Filters

Some advocates of defect prevention claim that zero defects is not only a useful motivation, but also an attainable goal.  In my post The Asymptote of Data Quality, I quoted Daniel Pink’s book Drive: The Surprising Truth About What Motivates Us:

“Mastery is an asymptote.  You can approach it.  You can home in on it.  You can get really, really, really close to it.  But you can never touch it.  Mastery is impossible to realize fully.

The mastery asymptote is a source of frustration.  Why reach for something you can never fully attain?

But it’s also a source of allure.  Why not reach for it?  The joy is in the pursuit more than the realization.

In the end, mastery attracts precisely because mastery eludes.”

The mastery of defect prevention is sometimes distorted into a belief in data perfection, into a belief that we can not just build a better mousetrap, but we can build a mousetrap that could catch all the mice, or that by placing a mousetrap in our garage, which prevents mice from entering via the garage, we somehow also prevent mice from finding another way into our house.

Obviously, we can’t catch all the mice.  However, that doesn’t mean we should let the mice be like Pinky and the Brain:

Pinky: “Gee, Brain, what do you want to do tonight?”

The Brain: “The same thing we do every night, Pinky — Try to take over the world!”

My point is that defect prevention is not the same thing as defect elimination.  Defects evolve.  An excellent example of this is spam.  Even conservative estimates indicate almost 80% of all e-mail sent world-wide is spam.  A similar percentage of blog comments are spam, and spam generating bots are quite prevalent on Twitter and other micro-blogging and social networking services.  The inconvenient truth is that as we build better and better spam filters, spammers create better and better spam.

Just as mousetraps don’t eliminate mice and spam filters don’t eliminate spam, defect prevention doesn’t eliminate defects.

However, mousetraps, spam filters, and defect prevention are essential proactive best practices.

 

There are No Lines of Causation — Only Loops of Correlation

There are no root causes, only strong correlations.  And correlations are strengthened by continuous monitoring.  Believing there are root causes means believing continuous monitoring, and by extension, continuous improvement, has an end point.  I call this the defect elimination fallacy, which I parodied in song in my post Imagining the Future of Data Quality.

Knowing there are only strong correlations means knowing continuous improvement is an infinite feedback loop.  A practical example of this reality comes from data-driven decision making, where:

  1. Better Business Performance is often correlated with
  2. Better Decisions, which, in turn, are often correlated with
  3. Better Data, which is precisely why Better Decisions with Better Data is foundational to Business Success — however . . .

This does not mean that we can draw straight lines of causation between (3) and (1), (3) and (2), or (2) and (1).

Despite our preference for simplicity over complexity, if bad data was the root cause of bad decisions and/or bad business performance, every organization would never be profitable, and if good data was the root cause of good decisions and/or good business performance, every organization could always be profitable.  Even if good data was a root cause, not just a correlation, and even when data perfection is temporarily achieved, the effects would still be ephemeral because not only do defects evolve, but so does the business world.  This evolution requires an endless revolution of continuous monitoring and improvement.

Many organizations implement data quality thresholds to close the feedback loop evaluating the effectiveness of their data management and data governance, but few implement decision quality thresholds to close the feedback loop evaluating the effectiveness of their data-driven decision making.

The quality of a decision is determined by the business results it produces, not the person who made the decision, the quality of the data used to support the decision, or even the decision-making technique.  Of course, the reality is that business results are often not immediate and may sometimes be contingent upon the complex interplay of multiple decisions.

Even though evaluating decision quality only establishes a correlation, and not a causation, between the decision execution and its business results, it is still essential to continuously monitor data-driven decision making.

Although the business world will never be totally predictable, we can not turn a blind eye to the need for data-driven decision making best practices, or the reality that no best practice can eliminate the potential for poor data quality and decision quality, nor the potential for poor business results even despite better data quality and decision quality.  Central to continuous improvement is the importance of closing the feedback loops that make data-driven decisions more transparent through better monitoring, allowing the organization to learn from its decision-making mistakes, and make adjustments when necessary.

We need to connect the dots of better business performance, better decisions, and better data by drawing loops of correlation.

 

Decision-Data Feedback Loop

Continuous improvement enables better decisions with better data, which drives better business performance — as long as you never stop looping the Decision-Data Feedback Loop, and start accepting that there is no such thing as a root cause.

I discuss this, and other aspects of data-driven decision making, in my DataFlux white paper, which is available for download (registration required) using the following link: Decision-Driven Data Management

 

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Bayesian Data-Driven Decision Making

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In his book Data Driven: Profiting from Your Most Important Business Asset, Thomas Redman recounts the story of economist John Maynard Keynes, who, when asked what he does when new data is presented that does not support his earlier decision, responded: “I change my opinion.  What do you do?”

“This is the way good decision makers behave,” Redman explained.  “They know that a newly made decision is but the first step in its execution.  They regularly and systematically evaluate how well a decision is proving itself in practice by acquiring new data.  They are not afraid to modify their decisions, even admitting they are wrong and reversing course if the facts demand it.”

Since he has a PhD in statistics, it’s not surprising that Redman explained effective data-driven decision making using Bayesian statistics, which is “an important branch of statistics that differs from classic statistics in the way it makes inferences based on data.  One of its advantages is that it provides an explicit means to quantify uncertainty, both a priori, that is, in advance of the data, and a posteriori, in light of the data.”

Good decision makers, Redman explained, follow at least three Bayesian principles:

  1. They bring as much of their prior experience as possible to bear in formulating their initial decision spaces and determining the sorts of data they will consider in making the decision.
  2. For big, important decisions, they adopt decision criteria that minimize the maximum risk.
  3. They constantly evaluate new data to determine how well a decision is working out, and they do not hesitate to modify the decision as needed.

A key concept of statistical process control and continuous improvement is the importance of closing the feedback loop that allows a process to monitor itself, learn from its mistakes, and adjust when necessary.

The importance of building feedback loops into data-driven decision making is too often ignored.

I discuss this, and other aspects of data-driven decision making, in my DataFlux white paper, which is available for download (registration required) using the following link: Decision-Driven Data Management

 

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No Datum is an Island of Serendip

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Continuing a series of blog posts inspired by the highly recommended book Where Good Ideas Come From by Steven Johnson, in this blog post I want to discuss the important role that serendipity plays in data — and, by extension, business success.

Let’s start with a brief etymology lesson.  The origin of the word serendipity, which is commonly defined as a “happy accident” or “pleasant surprise” can be traced to the Persian fairy tale The Three Princes of Serendip, whose heroes were always making discoveries of things they were not in quest of either by accident or by sagacity (i.e., the ability to link together apparently innocuous facts to come to a valuable conclusion).  Serendip was an old name for the island nation now known as Sri Lanka.

“Serendipity,” Johnson explained, “is not just about embracing random encounters for the sheer exhilaration of it.  Serendipity is built out of happy accidents, to be sure, but what makes them happy is the fact that the discovery you’ve made is meaningful to you.  It completes a hunch, or opens up a door in the adjacent possible that you had overlooked.  Serendipitous discoveries often involve exchanges across traditional disciplines.  Serendipity needs unlikely collisions and discoveries, but it also needs something to anchor those discoveries.  The challenge, of course, is how to create environments that foster these serendipitous connections.”

 

No Datum is an Island of Serendip

“No man is an island, entire of itself; every man is a piece of the continent, a part of the main.”

These famous words were written by the poet John Donne, the meaning of which is generally regarded to be that human beings do not thrive when isolated from others.  Likewise, data does not thrive in isolation.  However, many organizations persist on data isolation, on data silos created when separate business units see power in the hoarding of data, not in the sharing of data.

But no business unit is an island, entire of itself; every business unit is a piece of the organization, a part of the enterprise.

Likewise, no datum is an Island of Serendip.  Data thrives through the connections, collisions, and combinations that collectively unleash serendipity.  When data is exchanged across organizational boundaries, and shared with the entire enterprise, it enables the interdisciplinary discoveries required for making business success more than just a happy accident or pleasant surprise.

Our organizations need to create collaborative environments that foster serendipitous connections bringing all of our business units and people together around our shared data assets.  We need to transcend our organizational boundaries, reduce our data silos, and gather our enterprise’s heroes together on the Data Island of Serendip — our United Nation of Business Success.

 

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The Three Most Important Letters in Data Governance

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In his book I Is an Other: The Secret Life of Metaphor and How It Shapes the Way We See the World, James Geary included several examples of the psychological concept of priming.  “Our metaphors prime how we think and act.  This kind of associative priming goes on all the time.  In one study, researchers showed participants pictures of objects characteristic of a business setting: briefcases, boardroom tables, a fountain pen, men’s and women’s suits.  Another group saw pictures of objects—a kite, sheet music, a toothbrush, a telephone—not characteristic of any particular setting.”

“Both groups then had to interpret an ambiguous social situation, which could be described in several different ways.  Those primed by pictures of business-related objects consistently interpreted the situation as more competitive than those who looked at pictures of kites and toothbrushes.”

“This group’s competitive frame of mind asserted itself in a word completion task as well.  Asked to complete fragments such as wa_, _ight, and co_p__tive, the business primes produced words like war, fight, and competitive more often than the control group, eschewing equally plausible alternatives like was, light, and cooperative.”

Communication, collaboration, and change management are arguably the three most critical aspects for implementing a new data governance program successfully.  Since all three aspects are people-centric, we should pay careful attention to how we are priming people to think and act within the context of data governance principles, policies, and procedures.  We could simplify this down to whether we are fostering an environment that primes people for cooperation—or primes people for competition.

Since there are only three letters of difference between the words cooperative and competitive, we could say that these are the three most important letters in data governance.

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